Expansion full-convolution neural network and construction method thereof

Abstract

The invention discloses an expansion full-convolution neural network and a construction method thereof. The neural network comprises a convolution neural network, a feature extraction module, and a feature fusion module connected in order; the construction method comprises the following steps: selecting the convolution neural network; removing a full-connection layer and a classification layer forclassifying in the convolution neural network, and only leaving the middle convolution layer and a pooling layer, and extracting a feature map from the convolution layer and the pooling layer; constructing a feature extraction module, wherein the feature extraction module comprises multiple expansion upper-sampling modules connected in series, each expansion upper-sampling module respectively comprises a feature map merge layer, an expansion convolution layer and a deconvolution layer; and constructing a feature fusion module, wherein the feature fusion module comprises a dense expansion convolution block and a deconvolution layer. The expansion full-convolution neural network disclosed by the invention effectively solves the feature extraction and fusion problem in the convolution neuralnetwork, and can be applied to a pixel-level labelling task of an image.

Description

technical field [0001] The invention belongs to the technical field of image signal processing, in particular to an expanded fully convolutional neural network and a construction method thereof. Background technique [0002] Convolutional Neural Networks (CNNs) are the most widely used deep learning networks in image processing and computer vision. CNN was originally designed for image recognition and classification, that is, after the input image is passed through CNN, the category label in the output image is output. However, in some areas of image processing, it is not enough to just identify the category of the entire image. For example, in image semantic segmentation, it is necessary to label the category of each pixel in the image. At this time, the output is not a category label, but a map with the same size as the original image. Each pixel in the map is The semantic category to which the corresponding pixel in the original image belongs is marked. At this time, C...

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Problems solved by technology

[0005] However, the result map obtained from the previous fully convolutional network often cannot preserve the edge information of the object well, and the result map is often rough. Generally, a post-processing process is used to improve the labeling accuracy.

These post-processing processes not only increase the complexity of the labeling model, but because the labeling process is artificially divided, the results obtained are not smooth, and there are many discontinuous pixels, which have a great impact on the results.

These shortcomings are mainly because the previous FCN did not extract and utilize the image features in the network well, resulting in a decline in the resulting performance

On the other hand, the previous FCN has a large amount of parameters, which is not conducive to the transplantation and miniaturization of the model.

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